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Predictive Ability Assessment of Linear Mixed Models in Multienvironment Trials in Corn
Author(s) -
So YoonSup,
Edwards Jode
Publication year - 2011
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci2010.06.0338
Subject(s) - covariance , variance (accounting) , statistics , mixed model , variance components , linear model , data set , biology , best linear unbiased prediction , analysis of covariance , correlation , set (abstract data type) , predictive modelling , mathematics , computer science , selection (genetic algorithm) , machine learning , geometry , accounting , programming language , business
Prediction of future performance of cultivars is an important objective of multienvironment trials (MET). A series of linear mixed models with varying degrees of heterogeneous genotypic variance, correlation, and error variance structure were compared for their ability to predict performance in an untested environment in 51 data sets from the Iowa Crop Performance Test for corn ( Zea mays L.). In most cases there was no substantial improvement in predictions among models that included heterogeneity of genotypic variance–covariance components, but the best prediction model included heterogeneous environment‐specific error variances in 63% of data sets analyzed. The largest differences in predictive ability among models appeared to be due to poor estimation of genotypic covariance components in data sets with few common hybrids across 2 yr in a data set. Simulation confirmed the observation from cross validation. Our results suggested that predictions were not improved by modeling heterogeneous genotypic covariance components because of the small number of common hybrids across years. Inclusion of heterogeneous error variances did lead to slight improvements in predictions.